mlsanityData to 7 Oct 2026

Causal Physics Steering in Video World Models via Concept Activation Vectors

arXiv 2605.24322 ↗First posted 23 May 2026cs.CVcited by 4

On the map: World models → Latent predictive world models

Abstract (arXiv)

Video world models learn representations of physical dynamics, but controlling their physical expectations at inference time remains an open problem. Recent interpretability work identified a Physics Emergence Zone (PEZ), a group of middle transformer layers in VideoMAE where physical plausibility is represented separately from other visual features. However, it remained unclear whether this structure could be used to directly control the model's physics reasoning. We present physics steering, a training-free method that uses the weight vector of a linear probe at a PEZ layer as a Concept Activation Vector (CAV) and injects it into hidden states during inference. This shifts the model's physical expectations without changing any model weights. On the IntPhys benchmark, this intervention reliably shifts the model's plausibility judgment in either direction, depending on the steering sign. The effect appears only when the intervention is applied within the Physics Emergence Zone, suggesting that the relevant physics representation is localized there. We further find that physics is encoded separately from motion direction, and that different intuitive physics principles occupy distinct directions within this representation space. Together, these results show that physical reasoning in VideoMAE is not only readable, but also directly steerable.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 19 of its 25 references that are in the map. First posted on arXiv 23 May 2026.

Builds on

19 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2026Interpreting Physics in Video World Models4 Feb

Other references · 18

  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  2. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  3. 2017Neural Discrete Representation Learningcited by 4,650
  4. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  5. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
  6. 2017The Kinetics Human Action Video Datasetcited by 1,894
  7. 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,305
  8. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  9. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  10. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,046
  11. 2020Mastering Atari with Discrete World Modelscited by 990
  12. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
6 more
  1. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
  2. 2023MotionCtrl: A Unified and Flexible Motion Controller for Video Generationcited by 568
  3. 2023Learning Interactive Real-World Simulatorscited by 440
  4. 2024Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelscited by 412
  5. 2022Transformers are Sample-Efficient World Modelscited by 317
  6. 2021Embodied Intelligence via Learning and Evolutioncited by 154

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026CausalMotion: Structured Physical Reasoning as Keyframe and Trajectory Guidance for Training-Free Video Generation
  2. 2026WorldBench: Benchmarking Physical Understanding of World Models by Isolating Physics Concepts
  3. 2026CausalWM: Causal Chain-of-Thought Reasoning for Embodied World Model
  4. 2025TRAVL: A Recipe for Making Video-Language Models Better Judges of Physics Implausibility
  5. 2026PhysicsMind: Sim and Real Mechanics Benchmarking for Physical Reasoning and Prediction in Foundational VLMs and World Models
  6. 2026Distilling Physical Priors into Streaming World Models
  7. 2026Inference-time Physics Alignment of Video Generative Models with Latent World Models
  8. 2026PhysMRV: Physical Memory Retrieval and Verification for Physics Plausibility Reasoning